Forest disturbance regimes and trends in continental Spain (1985-2023) using dense landsat time series

被引:1
|
作者
Miguel, S. [1 ]
Ruiz-Benito, P. [1 ,2 ]
Rebollo, P. [2 ,3 ]
Viana-Soto, A. [4 ]
Mihai, M. C. [1 ]
Garcia-Martin, A. [5 ,6 ]
Tanase, M. [1 ]
机构
[1] Univ Alcala, Dept Geog & Geol, Environm Remote Sensing Res Grp, Colegios 2, Alcala De Henares 28801, Spain
[2] Univ Alcala, Dept Ciencias Vida, Grp Ecol & Restaurac Forestal FORECO, Alcala De Henares 28805, Madrid, Spain
[3] Univ Complutense Madrid, Dept Biodivers Ecol & Evolut, C Jose Antonio Novais 12, Madrid 28040, Spain
[4] Tech Univ Munich, Sch Life Sci Earth Observat Ecosyst Management, Hans Carl von Carlowitz Pl 2, D-85354 Freising Weihenstephan, Germany
[5] Ctr Univ Def Zaragoza, Acad Gen Mil, Ctra Huesca S N, Zaragoza 50090, Spain
[6] Univ Zaragoza, Dept Geog & Land Management, Geoforest IUCA, Pedro Cerbuna 12, Zaragoza 50009, Spain
关键词
Forest disturbance; Non-stand replacing; Global change; Time-series analysis; CCDC-SMA; Mediterranean; DETECTING TRENDS; GLOBAL CHANGE; DEGRADATION; COVER; VEGETATION; MORTALITY; CLASSIFICATION; DEFORESTATION; POLICY; ALBEDO;
D O I
10.1016/j.envres.2024.119802
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Forest disturbance regimes across biomes are being altered by interactive effects of global change. Establishing baselines for assessing change requires detailed quantitative data on past disturbance events, but such data are scarce and difficult to obtain over large spatial and temporal scales. The integration of remote sensing with dense time series analysis and cloud computing platforms is enhancing the ability to monitor historical disturbances, and especially non-stand replacing events along climatic gradients. Since the integration of such tools is still scarce in Mediterranean regions, here, we combine dense Landsat time series and the Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) method to monitor forest disturbance in continental Spain from 1985 to 2023. We adapted the CCDC-SMA method for improved disturbance detection creating new spectral libraries representative of the study region, and quantified the year, month, severity, return interval, and type of disturbance (stand replacing, non-stand replacing) at a 30 m resolution. In addition, we characterised forest disturbance regimes and trends (patch size and severity, and frequency of events) of events larger than 0.5 ha at the national scale by biome (Mediterranean and temperate) and forest type (broadleaf, needleleaf and mixed). We quantified more than 2.9 million patches of disturbed forest, covering 4.6 Mha over the region and period studied. Forest disturbances were on average larger but less severe in the Mediterranean than in the temperate biome, and significantly larger and more severe in needleleaf than in mixed and broadleaf forests. Since the late 1980s, forest disturbances have decreased in size and severity while increasing in frequency across all biomes and forest types. These results have important implications as they confirm that disturbance regimes in continental Spain are changing and should therefore be considered in forest strategic planning for policy development and implementation.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Boreal Shield forest disturbance and recovery trends using Landsat time series
    Frazier, Ryan J.
    Coops, Nicholas C.
    Wulder, Michael A.
    REMOTE SENSING OF ENVIRONMENT, 2015, 170 : 317 - 327
  • [2] United States Forest Disturbance Trends Observed Using Landsat Time Series
    Jeffrey G. Masek
    Samuel N. Goward
    Robert E. Kennedy
    Warren B. Cohen
    Gretchen G. Moisen
    Karen Schleeweis
    Chengquan Huang
    Ecosystems, 2013, 16 : 1087 - 1104
  • [3] United States Forest Disturbance Trends Observed Using Landsat Time Series
    Masek, Jeffrey G.
    Goward, Samuel N.
    Kennedy, Robert E.
    Cohen, Warren B.
    Moisen, Gretchen G.
    Schleeweis, Karen
    Huang, Chengquan
    ECOSYSTEMS, 2013, 16 (06) : 1087 - 1104
  • [4] An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks
    Huang, Chengquan
    Coward, Samuel N.
    Masek, Jeffrey G.
    Thomas, Nancy
    Zhu, Zhiliang
    Vogelmann, James E.
    REMOTE SENSING OF ENVIRONMENT, 2010, 114 (01) : 183 - 198
  • [5] Monitoring anthropogenic disturbance trends in an industrialized boreal forest with Landsat time series
    Pickell, Paul D.
    Hermosilla, Txomin
    Coops, Nicholas C.
    Masek, Jeffrey G.
    Franks, Shannon
    Huang, Chengquang
    REMOTE SENSING LETTERS, 2014, 5 (09) : 783 - 792
  • [6] Forest Disturbance Mapping Using Dense Synthetic Landsat/MODIS Time-Series and Permutation-Based Disturbance Index Detection
    Frantz, David
    Roeder, Achim
    Udelhoven, Thomas
    Schmidt, Michael
    REMOTE SENSING, 2016, 8 (04)
  • [7] MAPPING FOREST DISTURBANCE TYPES IN CHINA WITH LANDSAT TIME SERIES
    Huo, Lian-Zhi
    Tang, Ping
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 3086 - 3089
  • [8] Forest Disturbance Monitoring Based on Time Series of Landsat Data
    Zhong L.
    Chen Y.
    Wang X.
    Chen, Yunzhi, 1600, Chinese Society of Forestry (56): : 80 - 88
  • [9] How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?
    Meng, Yuanyuan
    Liu, Xiangnan
    Wang, Zheng
    Ding, Chao
    Zhu, Lihong
    ECOLOGICAL INDICATORS, 2021, 132
  • [10] Accurate mapping of forest types using dense seasonal Landsat time-series
    Zhu, Xiaolin
    Liu, Desheng
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2014, 96 : 1 - 11